Writing on Ai
Articles on Ai from Tauseef Fayyaz: practical writing on engineering, AI and tech careers, drawn from leading a team and mentoring engineers.
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6 posts tagged “ai”
Engineering Newsletters and Blogs Worth Your Inbox
Most engineers subscribe to fifteen newsletters, read none, and feel vaguely behind, because a full inbox produces guilt rather than learning. A shorter list sorted by what you would actually read each one for, why company engineering blogs beat most newsletters, and the weekly routine that makes any of it stick.
An AI Engineering Roadmap That Puts Things in the Right Order
Every AI engineering roadmap is the same twenty topics rearranged, and the arrangement is the only part that matters. Six stages from model basics through retrieval, measurement, tool calling and agents, each with something specific to build, what to skip entirely, and the two stages people skip and later regret.
Six Ways to Structure Work for an AI Agent
The word agent covers everything from a chatbot with one tool to a system that runs for twenty minutes deciding its own steps, and those do not fail the same way. Routing, parallelisation, orchestrator and workers, tool use, reflection and planning, what each is for, what goes wrong, and the durable execution work that separates a demo from something you can leave running.
RAG, Fine Tuning, or a Better Prompt: How to Choose
Every article recommends whatever that article happens to be about, and the real decision is more boring: match the problem to the cheapest technique that solves it, which is nearly always further down the list than people assume. A working order from prompting through retrieval, reranking, tools and agents, with the signal that tells you to move to the next step.
The AI Engineering Words You Keep Hearing, Explained Properly
The concepts underneath AI engineering are mostly not difficult, but every explanation assumes eight other terms you have not met yet. Tokens, embeddings, RAG, fine tuning, tool calling, MCP, agents, evals and the rest, each with what it is genuinely good for and where it will let you down, plus the order worth learning them in.
How to Use AI as a Companion, Not a Crutch
One rule decides whether AI tools make you faster or hollow you out: use them for work you could do yourself but would rather not, and for work you are actively trying to understand, never for work you cannot evaluate. Where they genuinely excel, where they quietly damage your skills, and the verification habits that separate engineers who benefit from those who accumulate debt.
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